Mobile Networks for Computer Go

TL;DR

This paper evaluates the effectiveness of Mobile Networks in the game of Go, analyzing their accuracy, efficiency, and impact on playing strength compared to traditional architectures.

Contribution

It introduces the use of Mobile Networks with separate policy and value heads for Go, assessing their performance and efficiency in supervised learning settings.

Findings

Mobile Networks achieve competitive accuracy in Go.

Separate policy and value heads improve learning performance.

Mobile Networks are more efficient with fewer parameters.

Abstract

The architecture of the neural networks used in Deep Reinforcement Learning programs such as Alpha Zero or Polygames has been shown to have a great impact on the performances of the resulting playing engines. For example the use of residual networks gave a 600 ELO increase in the strength of Alpha Go. This paper proposes to evaluate the interest of Mobile Network for the game of Go using supervised learning as well as the use of a policy head and a value head different from the Alpha Zero heads. The accuracy of the policy, the mean squared error of the value, the efficiency of the networks with the number of parameters, the playing speed and strength of the trained networks are evaluated.